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Source for the breeding of soft spring wheat in the conditions of Kirov region

2021· article· en· W3209150678 on OpenAlexaboutno aff
О. С. Амунова, Л. В. Волкова, Е. В. Зуев, А. В. Харина

Bibliographic record

VenueAgricultural science Euro-North-East · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Productivity and Crop Improvement
Canadian institutionsnot available
FundersMinistry of Science and Higher Education of the Russian Federation
KeywordsGeographyGrain yieldYield (engineering)Genetic resourcesAgronomyResistance (ecology)Winter wheatGrowing seasonBiologyHorticultureBiotechnologyPhysics

Abstract

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In the conditions of the Kirov region, 375 samples of soft spring wheat of various ecological and geographical origin (from 30 countries) of the Federal Research Centre N. I. Vavilov All-Russian Institute of Plant Genetic Resources (VIR) collection were studied. The assessment of the source material by yield, duration of the growing season, plant height, lodging resistance, productivity of the ear and plant, 1000-grain mass is presented in the form of a 9-point system of the severity of the trait for three years of field study. As sources for breeding, varieties that combine high yield (5.0...9.0 points) with a high level of economically valuable traits (7.0...9.0 points) were identified. The maximum average point (9.0) for yield capacity was noted by the variety Karabalykskaya 91 (Kazakhstan). The optimal height (53.9...74.0 cm) and lodging resistance were noted in the varieties: Estivum 155 (Russia), Kvorum (Ukraine), Epos, Ethos, Nandu, Schenk and KVS Akvilon (Germany), AC Phil and PT-741 (Canada), Hybrid (Mexico), Leguan (Czech), PS-133 (China) and SSL 25-2 (USA). Early maturation was distinguished in the domestic varieties Skala and Iren (80...87 days). The samples Line 3691h, Izida, FPCh-Ppd-m, Gerakl, Sibirskaya 16 (Russia), Kvorum (Ukraine), Attis and Nandu (Germany), Musket (England) were distinguished by the length (7.3...8.8 cm) and the number of grain of the main ear (32.1...39.9 pcs.). The varieties Voronezhskaya 16, Gerakl, Saratovskaya 72, Saratovskaya 73, Sibirskaya 16, Serebristaya, FPCh-Ppd-m and Ekada 6 (Russia), Anshlag and Kvorum (Ukraine), AC Gabrieland Hoffman (Canada), Attis and Nandu (Germany) were distinguished by a high grain weight per ear (1.28...1.58 g). The varieties Zakamskaya (Russia), Rassvet (Belarus), AC Cadillac (Canada) and PS-95 (China) are recommended as sources of high protein content. It is shown that the wheat yield in the region is closely related to the plant height (r = 0.67), the ear and plant productivity elements (r = 0.24...0.41), the protein content (r = -0.49) and does not depend on the 1000-grain mass (r = 0.04). Sixteen samples were identified with complex resistance to dominating fungal diseases. Samples Altajskaya 110, Kinelskaya 61, Line 2, Lutescens 30 and Estivum V313 (Russia) combined high yield with aluminum resistance. Varieties Altajskaya 100, Baganskaya 95, Line 3691h, Novosibirskaya 20 and Estivum 155 (Russia), Klein Vencedor (Argentina) and NOS Norko (Germany) were characterized by high yield and drought resistance. Based on the obtained results, a bank of sources of economically valuable traits was created, which allows to involve genotypes adapted to the conditions of the region in the breeding process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.217
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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